地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (12): 2128-2138.doi: 10.12082/dqxxkx.2021.210397

• 地球信息科学理论与方法 • 上一篇    下一篇

Blondel和k-核分解混合算法相结合的网络空间点群要素多尺度模型构建

王续盘(), 张衡*(), 周杨, 胡校飞, 彭杨钊, 齐凯   

  1. 战略支援部队信息工程大学地理空间信息学院,郑州 450001
  • 收稿日期:2021-07-15 修回日期:2021-11-02 出版日期:2021-12-25 发布日期:2022-02-25
  • 通讯作者: *张 衡(1976— ),男,河南郑州,博士,副教授,主要从事摄影测量与遥感方向研究。 E-mail: 13783651715@163.com
  • 作者简介:王续盘(1997— ),男,甘肃白银人,硕士生,主要从事摄影测量与遥感方向的研究。E-mail: 1848125421@qq.com
  • 基金资助:
    国家重点研发计划项目(2016YFB0801301-2);国家重点研发计划项目(2016YFB0801303)

Multi-scale Model of Point Group Elements in Network Space Combined with Blondel and k-shell Decomposition Hybrid Algorithm

WANG Xupan(), ZHANG Heng*(), ZHOU Yang, HU Xiaofei, PENG Yangzhao, QI kai   

  1. Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China
  • Received:2021-07-15 Revised:2021-11-02 Online:2021-12-25 Published:2022-02-25
  • Supported by:
    National Key Research and Development Program of China(2016YFB0801301-2);National Key Research and Development Program of China(2016YFB0801303)

摘要:

随着人们对网络空间的依赖性不断增强,互联网技术与网络基础设施规模迅速发展。很难直接用数字或表格的形式对网络空间进行全局的规划与管理,并且不容易发现隐藏在网络空间中的一些关键信息。网络空间点群要素的多尺度模型构建对网络空间数据的多尺度分析和可视化具有非常重要的意义。本文以网络空间的特征为依据,在借鉴基于社团划分的网络空间分层算法和基于节点重要性的网络空间分层算法特点的基础上,提出了Blondel算法和k-核分解的混合算法相结合的网络空间点群要素多尺度模型构建算法。本算法通过自动社团划分,用同一社团内的节点合并构建新的网络,有效解决了基于节点重要性的网络空间分层算法自动化程度低的弊端。利用核心节点来代替整个社团结构,显著保留了网络空间中节点的属性。实验表明使用该算法可以使各个层次网络空间点群要素的综合比例降至30%以下,较好的实现了网络空间点群要素的聚类与分层,若将网络空间点群要素的多尺度模型应用于地理空间中,则可实现网络空间地图的多尺度绘制。

关键词: 多尺度模型, 多尺度模型, 网络空间, 网络空间, 社团划分, 社团划分, 节点的重要性, 节点的重要性, Blondel算法, Blondel算法, k-核分解, 点群要素, 网络空间地图, k-核分解, 点群要素, 网络空间地图

Abstract:

In the 21st century, with the increasing dependence of people on cyberspace, Internet technology and network infrastructure develop rapidly. The elements that make up the cyberspace are complex and the data of nodes are large. It is difficult to directly use the form of numbers or tables for the overall planning and management of cyberspace, and it is not easy to find some key information hidden in cyberspace. So it is very important to construct a multi-scale model of point group elements in cyberspace for multi-scale analysis and visualization of data in cyberspace. If the nodes and topological relations in the cyberspace are directly visualized, a large number of points overlap and lines cross, resulting in the confusion of the information in the cyberspace. In this paper, based on the community and hierarchical characteristics of cyberspace, and referring to the characteristics of cyberspace stratification algorithm based on community division and cyberspace stratification algorithm based on node importance, a multi-scale model building algorithm of point group elements in cyberspace is proposed, which combines Blondel algorithm and K-shell decomposition hybrid algorithm. By automatic community division and combining nodes in the same community to build a new network, this algorithm effectively solves the problem of low automation degree of cyberspace stratification algorithm based on node importance. Core nodes in the cyberspace are extracted by k-shell decomposition hybrid algorithm. Core nodes are used to replace the whole community structure, which significantly retains the attributes of nodes in the cyberspace. For example, basic attributes such as the number, importance, and geographical location of nodes in the cyberspace. Experiments show that this algorithm can make the comprehensive proportion of each level of network space point group elements less than 30%, and achieve the clustering and stratification of network space point group elements. Compared with Blondel's algorithm, it is found that the proposed algorithm can preserve the hierarchy of network space with a high degree of importance. The multi-scale model of cyberspace point group elements is applied to geographic space which reduces the consumption of system resource, and the map of network space is realized. The multi-scale model of cyberspace can provide a data synthesis method for generating multi-scale cyberspace map. The different levels divided by this algorithm have different node numbers and edge numbers. The multi-scale model of point group elements can also provide node data with different levels of detail, which provides the basis for multi-scale analysis of network space.

Key words: multi-scale model, cyberspace, community division, importance of nodes, blondel algorithm, k-shell decomposition, point group element, map of cyberspace, multi-scale model, cyberspace, community division, importance of nodes, blondel algorithm, k-shell decomposition, point group element, map of cyberspace